Directory Assistance Semantic Analysis for Speech Recognition
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Solution Overview
Problem
Current automated directory assistance systems face challenges in recognizing the various human expressions and permutations used to refer to entities, leading to inefficiencies and reduced recognition rates due to the inability to account for contextual information and colloquial variations in speech recognition processes.
Innovation Solution
The system performs semantic analysis on directory assistance data to generate reference variations by associating metadata with individual listings, transforming it to capture contextual information, and using this transformed metadata to create synonyms and presentation names that can be recognized by speech recognition engines, thereby enhancing the recognition of entities despite variations in human reference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional speech recognition engines use context-free grammar or statistical language modeling without contextual analysis, then the system structure remains simple, but the recognition rate decreases due to inability to account for human variations in referencing entities
Solution Approach 1:
The system performs preliminary semantic analysis on directory assistance data to generate reference variations and synonyms before speech recognition occurs. This advance preparation creates a comprehensive grammar that anticipates multiple ways users might refer to entities, thereby improving recognition rates without adding complexity during the actual recognition process
Solution Approach 2:
The patent introduces an intermediary layer between the directory assistance data and the speech recognition engine. This intermediary performs contextual analysis and generates transformed metadata with reference variations, acting as a mediator that translates raw data into a format that enhances recognition capability without requiring the speech recognition engine itself to become more complex
2Reliability
If the system generates comprehensive reference variations through semantic analysis, then the robustness of speech recognition improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs semantic analysis and generates reference variations in advance, before actual speech recognition queries are processed. This preliminary action allows the computationally intensive work of analyzing contextual information and generating synonyms to be completed beforehand, so that during actual operation, the system can quickly match user input against pre-generated reference variations without significant processing delays
3Measurement precision
If metadata is transformed to capture contextual information for generating reference variations, then the accuracy of entity recognition improves, but the complexity of data processing increases
Solution Approach 1:
The patent introduces a metadata transformation layer that acts as an intermediary between raw directory assistance data and the speech recognition system. This intermediary performs contextual analysis and generates transformed metadata with reference variations, simplifying the overall process by handling the complex data transformation in a dedicated layer rather than requiring complexity throughout the entire system
Solution Approach 2:
The system segments the data processing into distinct stages: original metadata extraction, contextual analysis, reference variation generation, and speech recognition matching. This segmentation allows each component to focus on a specific task, improving accuracy of entity recognition while managing processing complexity through modular organization
Data Source
AI summary
Methods and systems of performing user input recognition. A digital directory comprising listings is accessed. Metadata information is associated with individual listings describing the individual listings. The metadata information is modified to generate transformed metadata information. Therefore, the transformed metadata information is generated as a function of context information relating to a typical user interaction with the listings. Information is generated for aiding in an automated user input recognition process based on the transformed metadata information.